Daniel B. Neill Daniel B. Neill, Ph.D.

Associate Professor of Computer Science and Public Service1,2
Associate Professor of Urban Analytics3
Director, Machine Learning for Good Laboratory
New York University

1Courant Institute of Mathematical Sciences, Department of Computer Science
2Robert F. Wagner Graduate School of Public Service
3Center for Urban Science and Progress, Tandon School of Engineering

E-mail: firstname.lastname @ nyu.edu


I am delighted to announce the formation of the new Machine Learning for Good Laboratory (ML4G Lab) at New York University! Please watch this space for links to our lab webpage (currently under construction) and position announcements.

*** We are currently inviting applications for a two-year postdoctoral associate position in the ML4G Lab! Please apply for this position at this link, and the job description is also available here. In brief, we are looking for applicants with a strong record of interdisciplinary machine learning work, who have a passion both for developing novel machine learning methods and for applying these methods toward the public good. Applications should be received by February 28th for full consideration. ***

Our lab is focused on development of novel machine learning methods for addressing critical urban problems and improving public health, safety, and security. The lab's five main research areas include: methodological advances for pattern detection and prediction; early event detection and situational awareness; causal inference (e.g., detecting natural experiments at scale); fairness and equity in algorithmic decision-making; and optimizing, deploying, and evaluating targeted interventions for good. Key application areas include public health and disease surveillance; crime prediction and prevention; opioid and overdose surveillance; fairness in criminal justice; allocation of city services; healthcare best practices; environmental health and prevention; and conflict and human rights. We are particularly interested in solving challenging urban problems where off-the-shelf machine learning methods are insufficient and new innovations are required.



Very brief bio:

I am an Associate Professor of Computer Science and Public Service at New York University's Courant Institute Department of Computer Science and Robert F. Wagner Graduate School of Public Service. I am also Associate Professor of Urban Analytics at NYU's Center for Urban Science and Progress and director of the Machine Learning for Good Lab. Previously, I was Associate Professor of Information Systems in the Heinz College at Carnegie Mellon University, where I was the H.J. Heinz III College Dean's Career Development Professor and Director of the Event and Pattern Detection Laboratory. I received my Ph.D. in Computer Science from CMU in 2006. Before that, I received my B.S.E. from Duke University, M.Phil. from Cambridge University, and M.S. from Carnegie Mellon.

Slightly longer bio:

Daniel B. Neill is Associate Professor of Computer Science and Public Service at NYU’s Courant Institute Department of Computer Science and Robert F. Wagner Graduate School of Public Service, and Associate Professor of Urban Analytics at NYU’s Center for Urban Science and Progress, where he directs the Machine Learning for Good (ML4G) Laboratory. He was previously a tenured faculty member at Carnegie Mellon University’s Heinz College, where he was the Dean’s Career Development Professor, Associate Professor of Information Systems, and Director of the Event and Pattern Detection Laboratory. He received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from Carnegie Mellon University. Dr. Neill’s research focuses on developing new methods for machine learning and event detection in massive and complex datasets, with applications ranging from medicine and public health to law enforcement and urban analytics. He works closely with organizations including public health, police departments, hospitals, and city leaders to create and deploy data-driven tools and systems to improve the quality of public health, safety, and security, for example, through the early detection of disease outbreaks and through predicting and preventing hot-spots of violent crime. He is also the Associate Editor of four journals (IEEE Intelligent Systems, Decision Sciences, Security Informatics, and ACM Transactions on Management Information Systems). He was the recipient of an NSF CAREER award and an NSF Graduate Research Fellowship, and was named one of the "top ten artificial intelligence researchers to watch" by IEEE Intelligent Systems.


Research:

My research is focused on novel statistical and computational methods for discovery of emerging events and other relevant patterns in complex and massive datasets, applied to real-world policy problems ranging from medicine and public health to law enforcement and security. Application areas include disease surveillance (e.g., using electronically available public health data such as hospital visits and medication sales to automatically identify and characterize emerging outbreaks), law enforcement (e.g., detection and prediction of crime patterns using offense reports and 911 calls), health care (e.g., detecting anomalous patterns of care which significantly impact patient outcomes), and urban analytics (e.g., helping city governments to predict and proactively respond to emerging patterns of citizen needs).

Selected publications by topic
Publications (chronological)
Presentations (chronological)
My CV
My Google Scholar page
CMU homepage (old)
CMU Event and Pattern Detection Lab page (old)

Which projects am I most excited about these days? So glad you asked! In no particular order: A detailed statement of my research interests (last updated October 2017) can be found here, and additional details can be found on my (old) EPD Lab project page.


Latest News:

I am delighted to announce the formation of the new Machine Learning for Good Laboratory (ML4G Lab) at New York University! We are currently recruiting postdoctoral fellows and graduate students.

Congratulations to CMU doctoral student, Mallory Nobles! Our abstract, Multidimensional Semantic Scan for Pre-Syndromic Disease Surveillance, was the winner of the 2018 International Society for Disease Surveillance Outstanding Student or Post-Degree Abstract Award.

Our pre-syndromic surveillance project was selected as the runner-up in the Department of Homeland Security's Hidden Signals Challenge, a nationwide system design competition which focuses on detecting emerging bio-threats in real time. Here is the link to the winner announcement.

I am guest co-editor of a special issue of GeoInformatica on "Analytics for Local Events and News". The submission deadline has been extended to January 15th, 2019. Please feel free to distribute this call for papers. Note that all papers should be submitted through the Springer GeoInformatica website.

Our rodent prevention work was recently featured in an article on CityLab. According to the article, "The city of Chicago is still running Neill's predictive analytics approach and has touted that it's 20 percent more effective than the traditional method of baiting rats after they've been discovered."

Our paper on Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams was named the winner of the Yelp Dataset Challenge. Our approach for identifying emerging topics can be used both for public health (detecting "novel" outbreaks with rare or previously unseen symptom patterns) as well as identifying emerging regional business trends. Thanks to both Yelp and CMU for their very nice press coverage of this work!

Our crime prediction work with the Pittsburgh Bureau of Police was featured in an editorial in the 30 Sep 2016 issue of Science.

Our comprehensive review article, "Youth violence: what we know and what we need to know", was featured in a press release by the American Psychological Association. The article was published in the January 2016 issue of the APA's flagship journal, American Psychologist, and is available here.



I gratefully acknowledge funding support from the National Science Foundation, grants IIS-0916345, IIS-0911032, and IIS-0953330, as well as a UPMC Healthcare Technology Innovation Grant, funding from the John D. and Catherine T. MacArthur Foundation and Richard King Mellon Foundation, and a gift from the Disruptive Health Technology Institute. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, UPMC, DHTI, Richard King Mellon Foundation, or MacArthur Foundation.

Last updated: 1/20/2019